Correction: Comparative GO: A Web Application for Comparative Gene Ontology and Gene Ontology-Based Gene Selection in Bacteria

Fruzangohar, Mario; Ebrahimie, Esmaeil; Ogunniyi, Abiodun D.; Mahdi, Layla K.; Paton, James C.; Adelson, David L.

doi:10.1371/journal.pone.0125537

Cited by 7 publications

(7 citation statements)

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“…C. Top biological processes. D. Comparison of the enrichment of cell cycle and apoptosis related categories in the three gynecological cancers as identified by Comparative GO [ 17 ] in the upregulated gene lists and in the E. downregulated gene lists. F. Pathways deregulated in all gynecological cancers are depicted in bar graphs.…”

Section: Resultsmentioning

confidence: 99%

“…Gene ontology classification and pathway annotation was performed with Expander 5.2 [ 21 , 22 ] and DAVID knowledgebase 6.7 [ 23 , 24 ] combining information from KEGG, REACTOME, BIOCARTA and PANTHER pathway databases. Additional comparison of the biological processes was performed with the use of Comparative GO [ 17 ] using all the upregulated and downregulated genes with their corresponding fold changes in each gynecological cancer type. Transcription factor binding site (TFBS) enrichment analysis was performed with both PRIMA algorithm and DAVID knowledgebase 6.7 [ 23 , 24 ] and transcription regulators were annotated when a motif was found close to a gene's transcription start site (TSS) in a region of 1200 bp (-1000, +200 bp).…”

Section: Methodsmentioning

confidence: 99%

“…Intriguingly, although common features emerge from studies among cancers of different origin regarding mutation rates, expression profiles or DNA methylation patterns [ 3 , 11 ], to our knowledge very few studies [ 12 – 14 ] have illustrated common biological functions or molecular mechanisms across different cancer types. Recently, a few comparative studies [ 15 , 16 ] utilizing the new profiling technologies in combination with the available bioinformatic tools [ 17 ] for the construction of biological networks, have identified common system–level properties among different cancer types [ 3 , 12 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Profiling of Discrete Gynecological Cancers Reveals Novel Transcriptional Modules and Common Features Shared by Other Cancer Types and Embryonic Stem Cells

et al. 2015

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Studies on individual types of gynecological cancers (GCs), utilizing novel expression technologies, have revealed specific pathogenetic patterns and gene markers for cervical (CC), endometrial (EC) and vulvar cancer (VC). Although the clinical phenotypes of the three types of gynecological cancers are discrete, the fact they originate from a common embryological origin, has led to the hypothesis that they might share common features reflecting regression to early embryogenesis. To address this question, we performed a comprehensive comparative analysis of their profiles. Our data identified both common features (pathways and networks) and novel distinct modules controlling the same deregulated biological processes in all three types. Specifically, four novel transcriptional modules were discovered regulating cell cycle and apoptosis. Integration and comparison of our data with other databases, led to the identification of common features among cancer types, embryonic stem (ES) cells and the newly discovered cell population of squamocolumnar (SC) junction of the cervix, considered to host the early cancer events. Conclusively, these data lead us to propose the presence of common features among gynecological cancers, other types of cancers, ES cells and the pre-malignant SC junction cells, where the novel E2F/NFY and MAX/CEBP modules play an important role for the pathogenesis of gynecological carcinomas.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Profiling of Discrete Gynecological Cancers Reveals Novel Transcriptional Modules and Common Features Shared by Other Cancer Types and Embryonic Stem Cells

et al. 2015

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“…To predict a novel type of regulatory networks based on GO interaction, a comparative GO was applied on common statistically significant regulated genes (Fruzangohar et al, 2015;Fruzangohar et al, 2017). Instead of gene relationships, the GO terms of biological processes were investigated for the construction of this type of network.…”

Section: Go Regulatory Network Constructionmentioning

confidence: 99%

Transcriptome signature of the lactation process, identified by meta-analysis of microarray and RNA-Seq data

Farhadian¹,

Rafat²,

Hasanpur³

et al. 2018

bta

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Lactation plays the crucial role in mammals' life. Uncovering the transcriptome signature of lactation process helps to understand the molecular basis of milk production. To identify the genes that express differentially between early and late lactation, publicly available microarray transcriptomic datasets of dairy cattle were investigated and the array results were validated by a next-generation sequencing dataset (RNA-Seq data from sheep). Limma and edgeR packages were used for the analysis of the microarray and RNA-Seq datasets, respectively. Five common differentially expressed genes (DEGs), namely glutathione s-transferase mu 3 (GSTM3 ), EGF containing fibulin-like extracellular matrix protein 1(EFEMP1 ), fibulin 1(FBLN1 ), gelsolin (GSN ), and fibrinogen-like 2 (FGL2 ), were identified. The involvement of EFEMP1 in the lactation process has been reported for the first time. The identified DEGs are involved in the development of the immune system and cell differentiation of the mammary gland. A gene ontology network analysis revealed the key role of the GSN gene in the regulation of two important functions of actin nucleation and barbed-end actin filament capping. The gene ontology enrichment analysis showed that the function of calcium ion binding is statistically (P < 0.05) enriched by the identified transcriptomic signature. The approach presented in this study provides an integrative framework for finding the signature of the lactation process by utilizing global transcriptome data, gene ontology (GO) regulatory network, and enrichment analysis.

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“…Certain studies employing novel profiling techniques integrated with existing bioinformatics methods have revealed systemic-level properties emphasizing on networks that instigate the interconnection of genes, proteins and metabolites whose dynamic interactions generate a corresponding function (7) in various types of cancer (5,6,(8)(9)(10)(11). Previously, one study identified four transcriptional modules associated with the cell cycle and apoptosis in cervical, endometrial and vulvar cancer (1).…”

Section: Introductionmentioning

confidence: 99%

Network analysis revealed aurora kinase dysregulation in five gynecological types of cancer

Sethi

Mishra

2017

Oncol Lett

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Abstract. Gene markers are crucial for cancer prognosis and treatment. Previous studies have placed greater emphasis on individual diagnostic genes, thereby ignoring systemic-level attributes across diseases. Female-specific cells namely, breast, endometrium, cervical, ovarian and vulvar cells are highly susceptible to cancer. To date, a limited number of molecular studies have been performed that evaluate common biological processes across gynecological types of cancer. Differentially expressed genes in breast, cervical, endometrial, vulvar and ovarian cancer were utilized to construct protein-protein interaction networks, and to identify a common module across the five cancer types. A single common module with 8 nodes and 26 edges was mined among the five cancer systems. In total, four hub genes were present across the five cancer gene sets. Genes in the common module were enriched for the common pathways and associated diseases. The aurora kinase pathway was revealed to be conserved across the five cancer types surveyed. The present study, therefore, revealed that the aurora kinase pathway has a crucial function in the pathogenesis of the five aforementioned gynecological types of cancer through cross-tumor conservation.

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Correction: Comparative GO: A Web Application for Comparative Gene Ontology and Gene Ontology-Based Gene Selection in Bacteria

Abstract: There is an updated URL for the Comparative GO website. The correct, complete URL for Comparative GO is: http://www.comparativego.com/. The Availability subheading within the Abstract should read as follows: http://www.comparativego.com/.

Cited by 7 publications

References 1 publication

Profiling of Discrete Gynecological Cancers Reveals Novel Transcriptional Modules and Common Features Shared by Other Cancer Types and Embryonic Stem Cells

Profiling of Discrete Gynecological Cancers Reveals Novel Transcriptional Modules and Common Features Shared by Other Cancer Types and Embryonic Stem Cells

Transcriptome signature of the lactation process, identified by meta-analysis of microarray and RNA-Seq data

Network analysis revealed aurora kinase dysregulation in five gynecological types of cancer

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